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Converging outer approximations to global attractors using semidefinite programming

Corbinian Schlosser 1 Milan Korda 1 
1 LAAS-MAC - Équipe Méthodes et Algorithmes en Commande
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : This paper develops a method for obtaining guaranteed outer approximations for global attractors of continuous and discrete time nonlinear dynamical systems. The method is based on a hierarchy of semidefinite programming problems of increasing size with guaranteed convergence to the global attractor. The approach taken follows an established line of reasoning, where we first characterize the global attractor via an infinite dimensional linear programming problem (LP) in the space of Borel measures. The dual to this LP is in the space of continuous functions and its feasible solutions provide guaranteed outer approximations to the global attractor. For systems with polynomial dynamics, a hierarchy of finite-dimensional sum-of-squares tightenings of the dual LP provides a sequence of outer approximations to the global attractor with guaranteed convergence in the sense of volume discrepancy tending to zero. The method is very simple to use and based purely on convex optimization. Numerical examples with the code available online demonstrate the method.
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Submitted on : Monday, June 1, 2020 - 5:01:31 PM
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Corbinian Schlosser, Milan Korda. Converging outer approximations to global attractors using semidefinite programming. Automatica, 2021, 134, pp.109900. ⟨10.1016/j.automatica.2021.109900⟩. ⟨hal-02709820⟩



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